A044080105

R.M.Teepika et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 4( Version 8), April 2014, pp.01-05
www.ijera.com 1 | P a g e
Multimedia Question Answering System Using Page Ranking
Algorithm
R.M.Teepika, P.J.Thanusu Prabha, Ms.N.D.Thamarai selvi
Computer Science Engineering, SRM Easwari Engineering College Chennai, India
Assistant Professor, M.E , SRM Easwari Engineering College, Chennai, India
Abstract—
In general, existing cQA forums provide only textual answers, which are not informative enough for many
questions. Conventional MMQA aims to seek multimedia answers without the assistance of textual answers.
This MMQA scheme using diversification method is able to enrich textual answers in cQA with appropriate
meta data which improves the informativeness and it bridges two gaps (i) The gap between questions and textual
answers (ii) The gap between textual answer and multimedia answer. This scheme consists of three components:
answer medium selection, query generation and data selection and presentation. This scheme determines the
type of media information that can be added for a textual answer. This scheme predicts the type of medium to be
added using Naïve Bayes Classifier, it automatically generates query based on QA knowledge and performs
multimedia search. Finally this scheme performs query adaptive re-ranking and duplicate removal to obtain a set
of images and videos for presentation along with textual answer. This scheme uses diversification methods to
make the enriched media data more diverse. This scheme uses Page Ranking algorithm to retrieve more relevant
and diverse search results.
Keywords- Multimedia Question Answering(MMQA), Community Question Answering(cQA), question
answering, media selection, Page ranking.
I. INTRODUCTION
QUESTION-ANSWERING (QA) is the
method for answering a query in a simple English
language [1]. It avoids the data contents that are vast
in quantity which are displayed as links in search
engines instead of getting the exact answers. In many
cases the results are good which are selected by
users. The information gainers are gaining the
information for certain specific questions in any topic
and get answers. The search engines are providing
the answers in a simple and effectively
understandable manner. The present procedure is
only providing the answers with textual answers
only. But it may not be sufficient and cannot easily
understand and they can get the data easily. In fig 1:
“How to make Orange juice concentrate?” It has
given only textual answers for getting the information
easily we are providing images and videos for any
query. We are adding multimedia contents to the
answers for getting the information. The automated
approach cannot be obtaining result for the users. [2]
Our aim is not straight forwardly displaying the
answer instead ranked answers with multimedia
contents. Based
Fig 1: An example from Ask.com
on the query given the top ranked answer is been
given.
Our idea is seen in solving the MMQA problem
by combining the user and human. Fig2: The output
of the media source is given as a proof for the better
answer medium selection. For multimedia data
selection and presentation we provide the image to
change the text to whether it is related to the human-
related. We are also searching for the cases where the
textual answers are not present.
RESEARCH ARTICLE OPEN ACCESS

Fig 2: An example of proposed system
II. PARALLEL EFFORT
A. Towards Mixed Media QA Against Text File QA
They are mainly focused on the intelligent
systems in certain domains. Based on the type of the
questions and expected answers we are providing
some sorts of QA into a. Open Domain QA, b.
Restricted Domain QA [1], c. Definitional QA and d.
List QA. In some cases automatic QA have
difficulties in providing the answers for complex
questions. For getting the technical knowledge any
user can seek idea and opinions. [2]. the existing
CQA system supports only textual answers which are
not much informative. Some users give multimedia
QA for getting the multimedia answer. The systems
have text based QA technology support the factoid
QA in the form of visuals of news videos and also in
text. Several videos uses text transcript derived video
OCR (Optical Character Recognition [4]. image
based QA [7] are specially for viewing the objects
[5]. The video based QA are given as media answers
from YouTube. Instead of giving multimedia data we
are providing the images and videos to enrich the
textual answers for users. This idea is for deal in with
many general questions to get better information.
B. Ranking Interactive Media Quest
The latest media search engines are used to build
the text information included with multimedia
entities such as titles and text. But the text
information has the content of images and videos and
this is for decreasing the search performance [9].
Rearranging is the method that is used for visual
information of images and videos. Existing
rearranging algorithms are mainly into two. They are
one is pseudo relevance [10]. Feedback and the other
is graph-based rearranging. The pseudo relevance
feedback approach is for latest results that are
relevant samples that are assumed as irrelevant. A
ranking model is mainly on pseudo relevant and
irrelevant samples that are used to rearrange the
original output. It provides the relevance feedback for
users to provide the feedback by the results that are
relevant or irrelevant.
The graph based rearranging [9][11] is usually on
two assumptions 1.the disagreement between the
initial and refined ranking list.2. The ranking
positions of similar samples are close. This approach
has a graph where the vertices are images and videos
and the edges reflect their pair wise. A graph based
learning process is then based on regularization
framework. Conventional methods have measures
based on colour, shape, texture. Here we categorize
the queries into two classes person-related and non-
person-related, and then we use the similarities for
different features for the different query type.
C. Perquisition About Interactive Media
The general problem is in finding the images from
the databases. These are easily tackle the video and
audio retrieval problems Multimedia search efforts
can be categorized into two types .They are text-
based search and content-based search. The text-
based search has textual queries to get media data by
matching the textual descriptions. Several media
websites are accumulated in media entities in text
based search. In the content based media retrieval, it
analyzes the contents of media data than metadata.
Instead the content based retrieval has some
limitations such as high cost, difficulty in visual
queries. Keyword based search are widely used in
media search. The intrinsic have commercial media
search engines for gap between the textual queries
and multimedia data.
III. INTERPRETATION MEDIA PICKING
The interpretation tells us whether textual is
needed or image or video. For example “When did
India became Republic” textual answer is sufficient,
“Who is the vice chancellor of America” image
answer is sufficient, “How to install Operating
System” video answer is sufficient [8]. The selection
of the answer is classified as a. Text b. Text + image
c. Text + video d. Text + image + video
Fig: 3 Proposed Architecture
A. Catechism Planted Categorizing
The answer is categorised into yes/no class “Are
you coming for the party”, choice class “Do you
like tea or coffee”, quantity class “When was the
first super frame computer made”, enumeration
class “Name of the oceans around India”, and
description class “What are the ways to reach
America”. Initially exploit the method in [6] to get

the informative word. We need only text answer for
yes/no, choice and quantity class., for enumeration
and description class text + image is sufficient; the
verb is needed to answer with text + video or text +
image + video. Based on the table 1 and 2 the
classification is been made. We use naive Bayes
classifier to get the appropriate answer.
TABLE 1
ILLUSTRATIVE QUESTIONING WORDS
Illustrative words Group
Be, there, have, when, will, can, how +
adj /adv, Why, how, where, who, to,
which, what
Text
TABLE 2
ILLUSTRATIVE CLASS PRECISE
ASSOCIATED WORDS
Grouping Class Precise Associated Words
Text
number, religions, website, country,
distance, speed, height, name,
period, times, age, date ,rate,
birthday, etc.,
Text +
Image
band, photo, what is a, place,
whom, surface, capital, largest,
pictures, logo, who, image, look
like, pet, clothes, image, appearance,
symbol, figure, etc.,
Text +
Video
recipe, differences, dance, first, said,
music, how can, invented, story,
how do, how to, ways, steps, film,
tell, songs, music, etc.,
A. Picking Established Depend on Populous
Affirmation
Media selection is by four class classification
model based on results of question based
classification, answer based classification, and
media resource analysis. Question based
classification is of four scores the question is
answered by “text”,” text + image”,” text + video”
,”text + image + video”. Answer based
classification is of four types. The media resource
analysis is of three scores which are for results of
text, image and video search.
B. Inquiry Procreation Pick And Launching
Collecting the exact image and video data from
the browser we are generating the queries from text
QA pairs for performing multimedia search
engines. We are using two steps. First is query
extraction. Textual questions and answers are
usually complex sentences. We are extracting the
keywords from the questions for querying. Second
is the query selection. We generate different
queries: one from question, one from answer, one
from combination of both question and answer
which is the most informative depends on QA
pairs, “How did the moon look like” There are
helpful keywords in the answer, “What is the
symbol of currency” Combining the question and
the answer to generate the efficient query, “Who is
the first president of China” for each QA pair, we
generate three queries. First convert the question to
query keywords. Second we identify several key
concepts which have major impact.. Thus we are
combing the two queries that are generated from
the question and answer. Hence there are three
queries.
The generated queries collect the images and
videos from the Google images and video search
engines respectively. Most of the search engines
area text based indexing and lot of irrelevant data.
We adopt the graph based rearranging method [8].
IV. PROPOSED SYSTEM
From the observations made in the previous
sections, we propose a system which produces
more informative search results by selecting the
media type automatically. The system has three
modules i) media selection ii) query generation, iii)
data selection and presentation.
a) Media selection
It is used to analyze the given QA pair; it predicts
whether the textual answer should be enriched with
media information and which kind of media data
should be added. Media data can be classified into
four classes: text, text + image, text + video, text +
image + video. This scheme will automatically
collect images, videos, or the combination of image
and video to enrich the original textual answer.
b) Query generation
It is used to collect the multimedia data;
we need to generate the informative queries. Given
a QA pair, this component extracts three queries
from the question, answer and QA pair,
respectively. The query is generated from the
question by removing the stop words using
stemming algorithm. The POS is generated using
Stanford Log Linear POS tagger and then the
histogram is calculated to select which query is
more suitable to get the relevant answers.

c) Data selection and presentation
This method vertically collects the images and
video data with multimedia search engines. We
then perform re-ranking and duplicate removal to
produce a set of accurate and representative
images and videos to enrich the textual answers.
For the given question proper media is selected and
refined search results are displayed using Page
ranking algorithm.
IV.EXPERIMENTAL RESULTS
Here the empirical evaluation is the
experimental settings such as data set and ground
truth labelling. There are two types of evaluation.
One is the local evaluation which is for
effectiveness for the components such as answer
medium selection, query generation, multimedia
data selection and presentation. The other is the
global evaluations which test the usefulness of the
media data.
TABLE 3
THE EFFICENCY RELATING OF QUESTION-
BASED CLASSIFICATION WITH VAIRANT
FEATURES.HERE “RELATED” MEANS
CLASS-PRECISE ASSOCIATED WORDS
FeaturesTesting
set
Google Picasa Both
Trigram 81.42% 85.99% 83.91%
Trigram + Head 85.37% 88.78% 87.20%
TABLE 4
THE EFFICIENCY RELATING OF ANSWER-
BASED CLASSIFICATION WITH VARIANT
FEATURES
FeaturesTesting
set
Google Picasa Both
Trigram 67.37% 71.30% 69.42%
Trigram +Verb 69.76% 74.82% 72.41%
A. .Upon the enlightening of enhanced media
data
All the complementary media data are
collected based on textual queries through which
we are getting from QA pairs. In other say queries
does not always reflect the original QA pairs. It
cannot reflect these media data answers the original
answer due to the gap between QA pair and
generated query. It also checks to estimated
whether the media data can answer the question.
There are 3 score candidates 2, 1, 0.The 3 score
candidates are the media sample can perfectly,
partially and cannot answer the question
respectively. We by chance select 200 QA answer
for enriched media data for appraisal. The results
actually indicate that for as a minimum 80.29%
question that exist enrich media data that can well
answer the questions. The average rating score is
1.266.
B. Upon the non appearance of textual quick
fix
In the future proposal, the existing
community contributed textual answers plays an
important role in the question understanding. So,
here the question is whether the favour can deal
with and perform when there are no textual answers
so far. First we need to remove the informational
clues from the textual answers in the answer
medium selection and multimedia query generation
mechanism. Here we additional examine the
performance of the scheme without textual
answers. We compare the performance of the
answer medium selection with and without textual
answers. It can examine that without textual
answers, and the classification correctness will
degrade by more than 5% for the answer medium
selection. Based on the 400 QA pairs mentioned ,
we compare the in formativeness of the obtain
media data with and without using textual answers.
We can see that without textual answers
the score will humiliate from 1.266 to1.099.The
move toward without textual answers can be

regarded as a conventional MMQA approach which is
directly finding the multimedia answers based on the
question. We assume the new settings present the
user revise results. The answers are not as revealing as
those generated with the textual answers; they are still
very revealing with clean textual answers.
C. Assessment of Ranking
To calculate the methods of judging whether the QA
pair is a person relevant or non person irrelevant .We
choose five hundred QA pairs from dataset randomly.
Then we learn SVM model with RBF kernel based on
seven dimensional facial character tics in order to
evaluate our query adaptive storage we first select
random 25 quires from person relevant ones for each
question .150 images or video converted rearranging.
Figure 3 illustrates the image search performance [3]
comparison from other to our proposed approach. The
convention method [3] only uses comprehensive
feature denoted as convention. Figure 4 illustrates the
video search performance comparison from other to
our proposed approach. Figure 5 illustrates Results of
Interactive answering for “who is the chancellor of
China” “text + image”. Query adaptive rearranging
[3] with text based classification. From the result we
see two query based adaptive method and consistent
outperform convention uses global features. Fig: 6
illustrates the Relating of overall average enlightening
count between with textual and without textual
answers.
Figure 4: The image search performance comparison
from other to our proposed approach
Figure 5: The video search performance comparison
from other to our proposed approach
V. CONCLUSION AND FORTHCOMING
PERFORMANCE
In this paper ,we portray the encouragement and
transformation of MMQA, its scrutinize
approach, we propose a unique contrivance to
answer questions using media data by ever
again textual answers in cQA.For the given
query our contrivance first foretell which type
of media is relevant for endowing the original textual
answer. Finally page ranking is carried out to retrieve
images and videos. Hence the appropriate answer can
be got in an efficient way. In our pondering we
heeded inappropriate answer because while doing
reranking the optimal solution is got. It would be
better if we use some other new method to perform
ranking for retrieving images.
VI. REFERENCES
[1]. D. Molla and J. L. Vicedo,”Question answering
in restricted domains: An overview,” Computat
Linguist, vol.13 no 1, pp.41-61, 2007.
[2]. L. A. Adamic, J. Zhang, E. Bakshy, and M. S.
Ackerman, ”Knowledge sharing and Yahoo
answers: Everyone knows something,” in Proc.
Int. World Wide Conf., 2008.
[3]. L. Nie, M. Wang, Z. Zha, G.Li, and
T.S.Cgua,”Multimedia answering: Enriching
text QA with media information,” in Proc.ACM
Int. SIGIR Conf., 2011
[4]. Y.C. Wu and J.C.Yang,”A robust passage
retrieval algorithm for video question
answering”IEEE Trans.Circuits Syst.Video
Technolo., vol.18
, no.10,pp 1411-1421,2008
[5]. T.S.Chua,R.Hong,G.Li,and J.Tang,”From Text
question-answering to multimedia QA on web-
scale media resources,” in Proc.ACM Workshop
Large-scale Multimedia Retrieval and
Mining,2009.
[6]. A.Tamura, H.Takamura, N.Okumara,
”Classification of Multiple-sentence questions,”
in Proc.Int.Joint Conf.Natural Language
Processing.2005.
[7]. G. Li, H. Li, Z. Ming, R. Hong, S. Tang, and T.
–S. Chua, “Question Answering over
community contributed web video”, IEEE
Multimedia, vol. 17, No. 4, PP. 46-57, 2010.
[8]. J. Zhang, R. Lee, and Y. J. Wang, “Support
Vector Machine Classification for Microarray
Expression Data set,” In Proc. Int. Conf.
Computational Intelligence and Multimedia
Applications, 2003.
[9]. X.Tian, L.Yang, J.Wang, Y.Yang, X.Wu, and
X.S.Hua,”Bayaesian video search rearranging,”
in Proc.ACM Int.Conf.Multimtdia 2008.
[10]. R.Yan, A.Hauptmann, and R.Jin,”Multimedia
search with pseudo relevance feedback,” in
Proc.ACM Int.Conf.Image and video Retieval,
2003 QA sites,”in.Proc. Int.Conf.Human factors
in computing systems, 2009.
[11]. M. Wang, K. Yang, X. – S. Hua, and H. – J.
Zhang, “Towards a relevant and diverse search
of social images,” IEEE Trans. Multimedia, Vol.
12, No.8,PP.829-842,2010.
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